{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0824d471",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-05T13:10:40.223520Z",
     "start_time": "2021-12-05T13:10:40.221143Z"
    }
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77166efe",
   "metadata": {},
   "source": [
    "The following example shows how to use TransBigData to download subway lines and to build a topological network model for the subway line network"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7f7cea6",
   "metadata": {},
   "source": [
    "# Download subway lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ab19d365",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-05T13:10:44.081986Z",
     "start_time": "2021-12-05T13:10:40.224996Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "获取城市id: 厦门成功\n",
      "1号线成功\n",
      "2号线成功\n",
      "3号线成功\n"
     ]
    }
   ],
   "source": [
    "import transbigdata as tbd\n",
    "line,stop = tbd.getbusdata('厦门',['1号线','2号线','3号线'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c012edaf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-05T13:10:44.621061Z",
     "start_time": "2021-12-05T13:10:44.084233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "line.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c1ed4118",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-05T13:10:44.723752Z",
     "start_time": "2021-12-05T13:10:44.622401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stop.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b61028af",
   "metadata": {},
   "source": [
    "# Splitting the metro line into sections"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a58774a",
   "metadata": {},
   "source": [
    "The `tbd.split_subwayline` method can be used to slice the metro line with metro stations to obtain metro section information (This step is useful in subway passenger flow visualization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "575aa0c6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-05T13:10:44.943758Z",
     "start_time": "2021-12-05T13:10:44.724912Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "metroline_splited = tbd.split_subwayline(line,stop)\n",
    "metroline_splited.plot(column = 'o_project')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "454296df",
   "metadata": {},
   "source": [
    "# Modeling for subway network topology"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1616a38b",
   "metadata": {},
   "source": [
    "We can also use the metro station data to build a topology model of the metro network. This step is useful for subsequent identification of metro travel paths. The graph generated relies on NetworkX."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b02b9ee0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-12-05T13:10:45.038118Z",
     "start_time": "2021-12-05T13:10:44.945132Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Modeling for subway network topology\n",
    "import networkx as nx\n",
    "G = tbd.metro_network(stop)\n",
    "nx.draw(G)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
